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This paper proposes CRANE - Concept Ranking According to Negative Exemplars - for semantic two-class segmentation of weakly labeled videos. The task can be summarized as follows: Given an oversegmentation of a video, tagged weakly with a concept such as "cat" or "dog", decide which segments actually belong to the concept. As known from semantic segmentation of weakly-labeled images, common difficulties are high in-class variation of concepts like "cat" and "dog" as well as the unknown location of the concept. In videos, an additional difficulty is the unknown temporal location of the concept.